bdappv

	---
	license: cc-by-4.0
	task_categories:
	- image-segmentation
	- image-classification
	language: []
	tags:
	- solar-panels
	- photovoltaic
	- remote-sensing
	- aerial-imagery
	- segmentation
	- distribution-shift
	- france
	- belgium
	pretty_name: BDAPPV
	size_categories:
	- 10K<n<100K
	configs:
	- config_name: google
	data_files:
	- split: train
	path: google/train-*
	- split: validation
	path: google/validation-*
	- split: test
	path: google/test-*
	- config_name: ign
	data_files:
	- split: train
	path: ign/train-*
	- split: validation
	path: ign/validation-*
	- split: test
	path: ign/test-*
	dataset_info:
	- config_name: google
	features:
	- name: identifiant
	dtype: string
	- name: image
	dtype: image
	- name: mask
	dtype: image
	- name: has_mask
	dtype: bool
	- name: split
	dtype: string
	- name: surface
	dtype: float32
	- name: azimuth
	dtype: float32
	- name: tilt
	dtype: float32
	- name: kWp
	dtype: float32
	- name: departement
	dtype: int32
	- name: city
	dtype: string
	- name: dateInstalled
	dtype: string
	- name: typeInstallation
	dtype: int32
	- name: countArrays
	dtype: int32
	- name: countInverters
	dtype: int32
	- name: isIntegrated
	dtype: bool
	- name: selfConsumption
	dtype: bool
	splits:
	- name: train
	num_bytes: 2447133010
	num_examples: 20707
	- name: validation
	num_bytes: 441348728
	num_examples: 3817
	- name: test
	num_bytes: 451607704
	num_examples: 3884
	download_size: 3341481346
	dataset_size: 3340089442
	- config_name: ign
	features:
	- name: identifiant
	dtype: string
	- name: image
	dtype: image
	- name: mask
	dtype: image
	- name: has_mask
	dtype: bool
	- name: split
	dtype: string
	- name: surface
	dtype: float32
	- name: azimuth
	dtype: float32
	- name: tilt
	dtype: float32
	- name: kWp
	dtype: float32
	- name: departement
	dtype: int32
	- name: city
	dtype: string
	- name: dateInstalled
	dtype: string
	- name: typeInstallation
	dtype: int32
	- name: countArrays
	dtype: int32
	- name: countInverters
	dtype: int32
	- name: isIntegrated
	dtype: bool
	- name: selfConsumption
	dtype: bool
	splits:
	- name: train
	num_bytes: 3204106988
	num_examples: 11526
	- name: validation
	num_bytes: 875106431
	num_examples: 3206
	- name: test
	num_bytes: 694527761
	num_examples: 2593
	download_size: 4783574371
	dataset_size: 4773741180
	---

	# BDAPPV — Aerial Images of Rooftop Photovoltaic Installations

	BDAPPV is a dataset of aerial images of rooftop PV installations in France and Belgium,
	with segmentation masks and installation metadata. Images are provided by two aerial
	imagery providers (Google and IGN), making it suitable for both segmentation/classification
	benchmarks and distribution shift evaluation across imagery sources.

	Paper: [Kasmi et al., Scientific Data, 2023](https://doi.org/10.1038/s41597-023-01951-4) — [arXiv:2209.03726](https://arxiv.org/abs/2209.03726)

	---

	## Dataset overview

	\| Provider \| Images \| Positifs (masks) \| Négatifs \| Note \|
	\|----------\|--------\|-----------------\|----------\|------\|
	\| Google \| 28,408 \| 13,303 \| 15,105 \| 399 images excluded (no metadata entry) \|
	\| IGN \| 17,325 \| 7,685 \| 9,640 \| \|

	- Images are 400×400 px PNG files.
	- Google images are a superset: every IGN installation also has a Google image.
	- Masks are binary PNGs (same resolution as images).

	---

	## Data structure

	```
	bdappv/
	├── google/
	│ ├── img/ # 28,408 images (28,807 raw − 399 excluded)
	│ └── mask/ # 13,303 segmentation masks
	├── ign/
	│ ├── img/ # 17,325 images
	│ └── mask/ # 7,685 segmentation masks
	├── annotations.csv # manifest: one row per (installation × provider)
	├── metadata.csv # installation-level metadata
	└── README.md
	```

	---

	## Loading the dataset

	```python
	from datasets import load_dataset

	# Google imagery (default)
	ds = load_dataset("gabrielkasmi/bdappv", "google")

	# IGN imagery
	ds = load_dataset("gabrielkasmi/bdappv", "ign")
	```

	Each example contains:

	```python
	{
	"identifiant": "OSIBG1RDEDJ", # installation ID
	"image": <PIL Image>, # 400×400 aerial image
	"mask": <PIL Image>, # segmentation mask (None if has_mask=False)
	"has_mask": True, # False = negative sample (no panel)
	"split": "train", # train / val / test
	"surface": 22.0, # panel surface (m²)
	"azimuth": -20.0, # panel azimuth (degrees)
	"tilt": 20.0, # panel tilt (degrees)
	"kWp": 3010.0, # peak power (Wp)
	"departement": 31, # French department code
	"city": "Castanet-Tolosan",
	"dateInstalled": "2007-09-01",
	...
	}
	```

	---

	## Recommended usage patterns

	### Segmentation (positives only)

	```python
	ds = load_dataset("gabrielkasmi/bdappv", "google")
	train_seg = ds["train"].filter(lambda x: x["has_mask"])
	# 13,303 images with masks across all splits
	```

	### Binary classification (panel / no panel)

	```python
	# Both providers have validated negatives
	ds_google = load_dataset("gabrielkasmi/bdappv", "google") # 13,303 pos / 15,105 neg
	ds_ign = load_dataset("gabrielkasmi/bdappv", "ign") # 7,685 pos / 9,640 neg
	# has_mask is the binary label (True = panel present)
	```

	### Distribution shift benchmark (cross-provider)

	The intended protocol for evaluating robustness to imagery distribution shift:

	```python
	train = load_dataset("gabrielkasmi/bdappv", "google", split="train")
	test = load_dataset("gabrielkasmi/bdappv", "ign", split="test")
	# Train on Google, evaluate on IGN — same installations, different sensors
	```

	Note: pooling both providers for training is not recommended as a default setup.
	Google and IGN images of the same installation share the same ground truth object;
	pooling them amounts to domain augmentation rather than independent data, and
	conflates the distribution shift signal. If you want to pool, build a custom
	dataloader merging both configs.

	---

	## Train / val / test split

	Split is based on spatial holdout by French department to prevent geographic
	leakage between splits. All Belgian and small-department installations are assigned
	to train.

	\| Split \| Installations \| Departments \|
	\|-------\|--------------\|-------------\|
	\| train \| 20,707 (73%) \| all others \|
	\| val \| 3,817 (13%) \| 3, 9, 11, 23, 44, 47, 52, 54, 59, 66, 72, 82, 88, 92 \|
	\| test \| 3,884 (14%) \| 2, 4, 6, 15, 16, 32, 38, 42, 51, 64, 67, 85, 91 \|

	The split is fixed and deterministic (seed=42). Do not re-split to ensure
	comparability with published results.

	---

	## Licenses

	This dataset combines components under different licenses:

	\| Component \| License \|
	\|-----------\|---------\|
	\| Segmentation masks & annotations \| [CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/) \|
	\| Installation metadata \| [CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/) \|
	\| Google aerial images \| [Google Earth Engine ToS](https://cloud.google.com/maps-platform/terms) — and underlying third-party imagery licensing (restrictions on redistribution apply) \|
	\| IGN aerial images \| [Etalab Open License 2.0](https://www.etalab.gouv.fr/licence-ouverte-open-licence/) — free incl. commercial use \|

	Important: the Google imagery restricts commercial use. For commercial applications,
	use the IGN configuration only (`load_dataset("gabrielkasmi/bdappv", "ign")`).

	---

	## Citation

	```bibtex
	@article{kasmi2023bdappv,
	title = {A crowdsourced dataset of aerial images with annotated solar
	photovoltaic arrays and installation metadata},
	author = {Kasmi, Gabriel and Saint-Drenan, Yves-Marie and Trebosc, David
	and Jolivet, Rapha{\"e}l and Leloux, Jonathan and Sarr, Babacar
	and Dubus, Laurent},
	journal = {Scientific Data},
	volume = {10},
	number = {1},
	pages = {59},
	year = {2023},
	publisher = {Nature Publishing Group},
	doi = {10.1038/s41597-023-01951-4}
	}
	```